Young Shick Ro

Brief paper: Modified Kalman filter for networked monitoring systems employing a send-on-delta method

In this paper, we consider a networked estimation problem in which sensor data are transmitted only if their values change more than the specified value. When this send-on-delta method is used, no sensor data transmission implies that the sensor value does not change more than the specified value from the previously transmitted sensor value. Using this implicit information, we propose a modified Kalman filter algorithm. The proposed filter reduces sensor data traffic with relatively small estimation performance degradation. Through experiments, we demonstrate the feasibility of the proposed filter algorithm.

An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator

Abstract Parallel manipulators have advantages like high accuracy, high stiffness, high payload capability, low moving inertia, and so on. In this paper, a detailed study to apply an online self gain tuning method using neural networks for nonlinear PD computed torque controller to a 2-dof parallel manipulator is presented. A novel nonlinear PD computed torque controller is achieved by combining conventional computed torque controller and auto tuning method using neural networks which has advantages such as flexibility, adaptation and learning ability. The proposed controller has a simple structure and little computation time while securing good performance in tracking trajectories of parallel manipulators. To verify the control performance, various simulations of a 2-dof parallel manipulator are conducted. Simulation results show the effectiveness of the proposed method in comparison with the conventional computed torque controller.

Quaternion-Based Indirect Kalman Filter Discarding Pitch and Roll Information Contained in Magnetic Sensors

This paper is concerned with orientation estimation using inertial and magnetic sensors. A new measurement equation is proposed for the quaternion-based indirect Kalman filter. Instead of using magnetic sensor data directly in the measurement equation, the data are modified so that the magnetic sensor data almost do not affect pitch and roll estimation during the measurement update. The proposed algorithms can be used for an application, where pitch and roll estimation is critical and there are large magnetic disturbances. Through an experiment and simulations, the proposed algorithms are compared with the standard method that uses magnetic sensor data for pitch and roll estimation. Using 50 simulation data, it is shown that pitch and roll estimation errors are significantly smaller when there are magnetic disturbances.

A robust fault detection and isolation scheme for robot manipulators based on neural networks

This paper investigates an algorithm to the robust fault detection and isolation(FDI) in robot manipulators using Neural Networks(NNs). Two Neural Networks are utilized: the first NN (NN1) is employed to reproduce the robots dynamic behavior, while the second NN (NN2) is used to achieve the online approximation for fault detection and isolation. This approach focused on detecting changes in the robot dynamics due to faults. An online monitoring is used not only to detect faults but also to provide estimates of the fault characteristics. A computer simulation example for a two link robot manipulator shows the effectiveness of the proposed algorithm in the fault detection and isolation design process.

H/sub 2/ networked servo control systems with time-varying delays

An H/sub 2/ servo controller is proposed for networked control systems. The network-induced delay is assumed to be time-varying and vary in the known range. The proposed controller guarantees stability and H/sub 2/ performance for all time-varying delay in the known range. The proposed controller is verified using a simple networked motor control system.

INS/GPS integration system with DCM based orientation measurement

This paper works toward the development and implementation of a INS/GPS integration system for the land vehicle application. A developed INS system is introduced to keep measuring the position/orientation of the vehicle when the vehicle is passed through GPS signal shading area. A new orientation scheme is studied to full fill the measurement states of the integration system. Roll/pitch estimation compensating external acceleration is performed with inertial sensors and yaw angle is obtained with GPS information. And then, the orientation information is supplied to the linearized Kalman filter of error model. This process is shown to improve the performance of the integration system. The field test was performed along a non-flat contour with some dismissals of GPS on it.

Fault Detection and Isolation in Wheeled Mobile Robot

This paper presents a fault detection and isolation scheme for wheeled mobile robots. A nonlinear observer is designed based on the mobile robot dynamic model. The fault is detected when at least one of the residuals exceeds its corresponding threshold. After that, three observers are activated to isolate three types of faults: right wheel fault, left wheel fault, and the other changed dynamic faults.

Networked estimation using a send-on-delta method

In this paper, we consider a networked estimation problem in which sensor data are transmitted only if their values change more than the specified value. When this send-on-delta method is used, no sensor data transmission implies that the sensor value does not change more than the specified value from the previously transmitted sensor value. Using this implicit information, we propose a modified Kalman filter algorithm. The proposed filter reduces sensor data traffic with relatively small estimation performance degradation. Through experiments, we demonstrate the feasibility of the proposed filter algorithm

Inertial sensor data compression using modified ADPCM

This paper is concerned with compression of multiple inertial sensor data. The compressed data consists of ADPCM compressed data and an adaptive refinement data. Each sensor data is first compressed using ADPCM. To reduce the quantization errors, refinement quantizers are then used, where the total number of bits for the refinement quantizers is fixed. Which inertial sensor data should be further refined is adaptively determined based on quantization error bounds. In the proposed method, total number of bits for the compressed data is fixed; however the number of bits for individual inertial sensor is adaptively determined. Through experiments, it is shown that the proposed method provides better compression quality than the standard ADPCM with the same total number of bits.

Robot Geometric Parameter Identification with Extended Kalman Filtering Algorithm

This paper proposes a calibration method for enhancing position accuracy of robotic manipulators. In order to increase the robot accuracy, the method first develops a robot kinematic model and then identifies the robot geometric parameters by using an extended Kalman filtering (EFK) algorithm. The Kalman filter has advantages in identifying geometric parameters from the noisy measurements. Therefore, the obtained kinematic parameters are more precise. A simulation study of this calibration is performed for a PUMA 560 robot to prove the effectiveness of the method in increasing robot position accuracy.